Previously published to Propelo.AI on the future of data and engineering excellence

Data is propelling new business. And its impact on the business community has, in a way, become an art form. And the art of understanding business data, or business intelligence (BI), is centered on a company’s ability to read, transform, optimize and present this data between both technical and non-technical teams. As a result, it empowers businesses to improve operations, optimize internal processes and evolve their workforce in ways they have never done before.

Engineering teams can make that happen. In fact, they are in a better position to drive data within the enterprise space than most other departments. They know data. They create data. And when they implement that data, they can really impact operations.

And who better to steer the business than engineering leaders? They set the pace for customer experiences. They offer value through each iteration. And without them, most businesses would never gain traction.

Engineering analytics are necessary for both internal and operational deployments. An engineer’s entire profession is based on gathering and implementing data. From efficiently managing projects to improving the software development lifecycle (SDLC), engineers know how to gather unique metrics and how to put them into action.

Over the years, various roles have been introduced into the data community. Some have been phased out or combined to create hybrid models with an entirely different set of responsibilities for very specific business needs. And similarly, some roles have emerged as solutions to the challenges faced by enterprise businesses that struggled to find the answers to their most pressing business, data and technology-related needs.

Data can significantly impact a company’s spending, improve customer touchpoints and steer teams into greater productivity.

Most businesses are focused on providing customers with the ultimate experience, which means that there is an increasing need for them to understand every step of the customer journey. As a result, data must be extracted, organized and curated into business-specific datasets, enabling key stakeholders and decision-makers to gain actionable insight into the inner workings of their business and to act accordingly in order to compete in a digital world.

There is an increased need for businesses to understand this data when delivering and personalizing digital products and services for consumers. Likewise, companies have begun incorporating data into their hiring processes and initiatives toward workforce optimization.

Many now consider analytics engineers the brains behind the “modern data stack.” They decide which tools to use for ETL/ELT and then set them up.

Big data is changing how we do business and creating a need for both data and analytics engineers who can skillfully collect and manage large quantities of data — often asking the right questions and thinking beyond the metric.

Data must be interpreted correctly to avoid flawed insight, ultimately disrupting an organization’s growth and stability. It must also be produced in a timely fashion.

Analytics is most notably defined as “a process of transforming data into actions through analysis and insight in the context of organizational decision-making and problem-solving.” Businesses are putting this data to work through the use of machine learning (ML), business intelligence (BI), artificial intelligence (AI) and statistical analysis (SA) — and making better decisions.

The engineering analytics team will then enhance the quality of this data through select metrics and actionable delivery. This not only allows engineers to focus on meeting and exceeding the needs of the end-user but also to collaborate with other departments to explore and improve inefficiencies companywide.

What is analytics engineering?

Data teams collaborate to produce groups of pristine data on behalf of business operations. First, they’ll take the company’s goals and objectives into account while preparing large datasets to measure its performance. Then, they’ll use and interpret the data to tell stories about how well the business is doing, how it could be doing better, where it’s falling short and where it’s overlooking opportunities amid daily operations. Business leaders will then take this data and turn it into both action and insight.

Data science is the means by which critical business information is gathered and interpreted, providing both the data analyst and engineer with a deeper understanding of the business they serve and serving that business with the ammunition they need to solve and identify problems internally.

There’s a fine line between data science and engineering analytics. And traditionally, there were three separate roles on the data team. While these roles still exist, the actual duties have changed over the years for roles such as:

Data Engineer
The data engineer was focused on the housing, structure and extraction of data. Their focus was on optimizing and organizing datasets over a specific period of time. They were also responsible for testing this data to make sure it was accurate and accessible across the data infrastructure. Their tools were much more complex than any other area of the data industry. Many were data engineers who built databases from scratch and organized the data within table schemas and spreadsheets.

Data Analyst
The data analyst was then responsible for assessing this data — often presenting it in a visual format to further discuss its findings with business and technology leaders within the organization(s) it served. The tools that they used were “simple to intermediate” at best. These were mostly pipeline tools that would allow them to present curated datasets while translating the results to decision-makers within the organization.

Data Scientist
Unlike data engineers and data analysts, data scientists have a much deeper understanding of the data behind the algorithms, often creating algorithms of their own. We’re seeing a rapid increase in technology used to capture data points internally. Machine learning and artificial intelligence are two tools used by data scientists to extract, organize and even predict future trends in data over time.

And then, technology evolved. There were suddenly more tools on the market — many of them automated. Many of them use advanced analytics functions. Most of them migrate to dating warehousing somewhere on the cloud.

When analytics engineering emerged as the newest career in data, people began asking, “When did analytics engineering become a thing?” Many of them wondered when their job title transitioned to or away from being a full-blown data engineer.

According to Datameer, “This new role is emerging due to the data engineering talent shortage, which is causing data engineering resources to be stretched thin and projects to get delayed.” Up until the pandemic, the vacancy rate was high for people unavailable to fulfill roles in the data industry — an industry expected to hit $655.53 billion in value by 2029.

Data engineer vs. analytics engineer – What’s the difference between the two?

The impact of data analytics is exponential across a wide range of industries and markets. Data is essential to how businesses operate and how they grow over time. Data helps companies to become more efficient, outperform their competition and maximize their budgets. They’ll know where each dollar needs to be spent — and how it could affect the organization.

When working with data, some common tasks are performed at all organizational levels. First, businesses will acquire and compile datasets that align with the needs of their business. They will also develop algorithms that allow them to transform data into useful, actionable information accessible by data engineers, data analysts and internal leadership teams.

They’ll build, test and maintain knowledge and self-service databases, pipeline architectures and data warehouses within cloud and hybrid environments. They’ll collaborate with leadership to better understand the company’s objectives. They’ll create new data validation methods and data analysis tools.

And finally, they’ll make sure that all the data is collected, secured and in compliance with data governance and regulations.

The role of an analyst engineer is similar to that of a traditional data scientist, whereas the analytics engineer has a more flexible role focused on making clean data available to data scientists and analysts — at scale.

Both roles use data mining, big data and machine learning (ML) to support quantitative decision-making. Expect the analytics engineer and the data engineer to be more hands-on than a data analyst.

Data engineers are a bit more code heavy than analytics engineers. They’re a bit more involved in ETL/ELT migrations. And although data engineering now involves automation, data engineers are more hands-on and engage in software engineering.

Analytics engineers, on the other hand, are quite database-heavy. They’re like the journalists of the data game. They ask the hard questions and form strong opinions. Their data is collected and organized for further reporting. They build pipelines and infrastructure.

They do the heavy lifting — much of it ad hoc in nature. They focus more on the business side of problems, as opposed to software engineering. They’ll interface with data analysts, who will then relay the data in the form of storytelling and custom reports.

Through automation, there’s no longer a need for redundant roles and responsibilities. While a data engineer is needed to understand the more technical aspects of an organization, it is the data scientist that is needed to interpret business information.

The analytics engineer came from the evolution of both roles. They’re great at communicating the results of their findings and business intelligence data. But they’re also good at developing pipeline strategies, including guiding the direction of research and development based on the ongoing needs and interests of the business.

The analytics engineer makes life easier for the others and keeps operations flowing smoothly for data teams and the organization.

What are analytics engineers, and what do they do?

The analytics engineer is a hybrid role that came about through the natural evolution of data. It’s a happy medium between data analysis and data engineering — and the maturation of data science over the years.

A successful analytics engineer has a fine blend of business acumen and technical expertise as he or she rotates between ongoing business strategies and frequent data development projects. This relatively new role is for professionals who’ll work within a data team to build dataset representations and tools.

He or she will spend most of their time focused on building data pipelines for business intelligence. As an investigator, they will draw a correlation between available resources, key performance indicators (KPIs) and business logic to provide clean datasets to the end user.

This means gathering, transforming, testing, deploying, documenting and then preparing the data to pass off to another party, who will then translate this data in the form of actionable discernment that can be applied to business decisions and strategies.

An analytics engineer models data in ways that will provide clarity and insight using SQL and other databases. End users understand this data simply by how it’s laid out and how simply it’s been presented.

Examples of data tracked by the analytics team include:

Customer segmentation
Maintaining low-cost operations
The status of resource utilization
Business performance
Internal issues for diagnosis and maintenance
Classification strategies
Inventory management
Sales and marketing data
Customer experiences
Data management itself

The analytics engineer is great at communicating their findings, explaining the strengths, weaknesses, opportunities and threats of business while pointing out the gaps in infrastructure.

He or she understands business as a whole yet can apply software engineering best practices like version control and continuous integration into the analytics code base in an effort to establish a quality control process for real-time monitoring and data collection.

The University of Wisconsin-Madison College of Engineering explains that outside of gathering data and building datasets, the analytics engineer will regularly engage in:

Machine learning and predictive analytics, where prediction meets personalization
Statistical methods and decision science
Visualization tools and techniques.
Optimization of products, including processes, research, design, testing and operations
Leadership and communication, effectively managing active change
Standardizing data points

Data is filtered before it reaches the end-user. The analytics engineer takes on raw data and tests it to ensure that it’s accurate. Upon validation, he or she will begin data modeling. Formal documentation will follow once the data has been validated.

The analytics engineer will train business users on how to use data visualization tools so that they can prepare and distribute custom reporting to other members of the organization.

He or she responds quickly to leadership requests, often collaborating and developing strategies for business transformation. Analytics engineers also leverage working data to provide developers with the continuous integration/continuous delivery (CI/CD) of tools and solutions they so desperately depend on.

They create datasets that allow the end-user to understand and evaluate the information provided within the data. Data engineers and data analysts will take this data and create a visual representation, or visualizations, of key data patterns and draw conclusions that will then be directed toward internal and external stakeholders, leadership and the end-user.

According to the ThoughtSpot Blog, these are the top skills required for analytics engineering:

SQL
Experience with dbt
Communication
Python
Experience with modern data stack tools

What does a data analytics engineer do?

Data engineers design and build systems for the sole purpose of gathering, managing and converting raw data into valuable information that will be interpreted by data scientists, business analysts and technical leadership teams alike. This data will then be translated at scale by the data analyst into actionable insight allowing executive leadership to make more informed decisions.

By making this data accessible to all key stakeholders within the organization, there is a greater sense of transparency across the enterprise. Data will be maintained at a higher level of accuracy across departments. With immediate, real-time access to this data, business leaders can make tougher, more educated decisions on the fly and continually work to optimize the internal business structure.

The data engineer uses table schemas to keep track of data and where it is stored.

Analytics engineering is a combination of data science and analysis. Engaging in best practice strategies for software engineering, analytics engineers ensure the continued availability, usability, integrity and security of data within enterprise systems.

Data engineers build data pipelines and perform extract, transform and load (ETL), behind the scenes, in an end-to-end data pipeline — automating scripts, extracting data from multiple sources, moving and transforming data before loading it into a centralized repository like a data warehouse or a data lake. And although they prepare data for others, they make sure that data remains highly available, consistent, secure and recoverable.

The data engineer corrects data ingestion and integrates on behalf of the internal data engineering community. Data is then combined and curated into several different datasets, maintained and optimized within a centralized cloud, or hybrid cloud, data infrastructure solution. As a result, this data is resilient, scalable and future-proofed to meet the needs of the end user.

It’s quite common that data engineers engage in regular data migration, moving data from one system to the next, especially in the case of new and upgraded technologies and as businesses sunset legacy systems they no longer have use for. And because of this, they often try to find the best and most dependable solutions to add to their tech stacks.

The data engineer understands the need for security and why it’s crucial to maintain optimal version controls. They use a wide range of machine learning, big data tools, coding and programming languages, such as SQL, NoSQL, Python, Java, R and Scala, in their day-to-day work cycles, which also consist of:

Minimizing risks associated with code deployment
Mapping and documenting data flow, source-to-target instructions
Implementing data flows for analytics and BI systems
Automating data-gathering processes
Re-engineering manual data flows
Reducing and phasing out redundancies
Support the build and repair of data systems
Determining how to convert data structures
Enabling scalability and reuse of data
Managing metadata within repositories
Analyzing an organization’s data assets

“The lines between these roles are blurry,” points out dbt, an organization that operates in the analytics engineering space.

“Some analytics engineers might spend time doing analyst work like deep dives,” the organization continues, “while others might be comfortable writing production-level Python code but realize doing so often isn’t the highest leverage use of their time.”

Top skills required for data engineering:

Python
AWS
Git
Bash
Spark
Hadoop

He or she also has the following in their data toolbelt:

A cloud data warehouse, or pipeline-as-a-storage (PaaS) tool, for data storage
Solutions for data extraction and loading
Data modeling tool for data transformation
A tool for modern business intelligence (BI)

What does a data analyst do?

Data analysts are not as code-heavy as the data or analytics engineers. But, they do engage in data assessment, offering businesses the insight and storytelling that allows them to evolve the business altogether. They answer questions based on data and deliver insight through business intelligence tools and solutions.

A data analyst presents the data that’s been mined and builds out dashboards for self-service capabilities and data-driven visualization. They answer the “what’s,” “where’s,” “whens’ and “why’s” behind business activities through causal insight.

Data analysts take over once the data engineer’s job is finished, working with business users to understand the exact data requirements necessary and to build critical dashboards based on a company’s ongoing forecasting strategies.

What is the importance of data analytics?

Data analytics are essential for reducing the cost of operations and improving business performance over time. It’s also important when monitoring a product’s performance and improving it for new markets. Data can help businesses stay ahead of their competition, predict future trends and fill gaps across operations. It can also help companies to improve their security, detect fraud and remove scrutiny.

What are the eleven most popular types of data analytics?

According to Builtin, there are six primary types of data analytics:

Through descriptive analysis, data teams can summarize the data in an easy, digestible way.
Exploratory data analysis allows businesses to unlock and discover the correlation between data variables, when and how they should take action.
Inferential analysis is used when generalizing a larger population with a smaller sample size of data.
Businesses should use predictive analysis to make healthy predictions about what the future holds and take action accordingly.
Causal analysis finds the answers to the “what” and “why” behind variables, emphasizing the cause between one or more data points and the effect of said data on the business.
Measuring the exact changes in variables that lead to other changes in other variables is made through mechanistic analysis.

Investopedia, the world’s leading source of financial content on the web, breaks down five more that have proven time and time again that businesses succeed through more specialized forms of data analysis:

Analyzing the relationship between dependent variables to determine how change may impact additional changes throughout the business is what is called regression analysis.
Factor analysis is when you take a large dataset and condense it into an even smaller dataset with the goal of uncovering hidden trends that would otherwise be difficult to see.
The process of breaking a dataset into groups of similar data, as with customer demographics, is cohort analysis, which promotes the deep dive of key subsets in order to draw quantitative conclusions.
The Monte Carlo Simulation Model defines the probability of different outcomes as they happen and allows users to take immediate action to prevent losses and mitigate risks.
Users can track data over time and solidify the relationship between the value of a data point and the occurrence of the data point, such as with financial information. This type of analysis is called time series analysis.

Together, both engineers and analysts can draw quick and simple correlations between key data sets and the information they are trying to find.

How do you use data analytics?

Understanding the analytical process is just as important as knowing the data. On the surface, it may just look like data, but a deep dive will indicate so much more to a business that’s looking to expand, grow and switch gears. Here are six examples of how data analytics are used effectively:

Businesses need to understand the problem — there’s always a problem.
Asking questions and gathering the right information takes businesses will take them just one step closer to finding a solution.
By analyzing this data, decision-makers can gain a better understanding of their business and make better decisions in real time.
Insights will generate a global view of the organization, where it can be further analyzed for its strengths, missed opportunities, weak spots and potential failures.
Deploying various models allows data to be synthesized for a number of demographics, business-related needs and case scenarios using the proper tooling and methodology.
Data teams monitor and optimize both static and real-time data to ensure accuracy at every step of the data cycle, promising accurate, up-to-date information to the end-user, mitigating issues before they occur and securing this data from outside breach or corruption.

What is the difference between ‘analytics engineering’ and ‘engineering analytics’?

Data science is excellent for analyzing different types of data in different ways. The accuracy of inference actually depends on its sampling scheme, such as how data is collected and what it will be used for like:

Managing risk and detecting fraud
Managing customer data and detecting anomalies
Personalizing and customizing data and content
Conducting market research and presenting findings
Analyzing and improving operations

If you haven’t figured it out by now, analytics engineering and engineering analytics are two totally different beasts — and while they’re related, they should definitely be treated as such.

The focus of analytics engineering is, at its core, to create self-service insights and industry-specific datasets based on the needs and interests of the business user. They create knowledge bases and dashboards full of intelligence data.

But in engineering analytics, the engineering team is the primary focus.

Engineering analytics is typically carried out by the software engineering team, and engineers use data to fundamentally understand their delivery funnel and which metrics can help them determine how well they are doing as a team. They’ll measure performance to understand how they’re getting better over time. They’ll measure speed and velocity to see how much more efficient they are between projects and identify the bottlenecks that are holding them back.

They’ll use metrics such as:

Visual trends that measure performance based on speed and quality
Customer segmentation for better resource utilization
Metrics that gauge developer productivity
Improved performance tracking for diagnosis and maintenance
Classification strategies for improved inventory management
Better use of sales data for increased revenues and customer satisfaction
Output and development times, assessing how long it takes for solutions to go to market
Friction points that hold up progress and delivery
DORA Metrics to gain insight into engineering team effectiveness
Descriptive and predictive analytics to learn what has happened and to predict what will happen
Code reviews to uncover and determine how quickly bottlenecks have been handled, as well as how often rework had to be implemented

Engineering performance is often correlated to:

How well they’re meeting the goals of the department
Whether or not their actions align with the broader goals of the business
The client’s perception of the enterprise, based on the delivery of software solutions
How long it takes for the team to respond to pull requests or clear out the backlog
How well they’re meeting and/or exceeding client expectations from a product standpoint
How well they’re mitigating risks or developing secure products, how they can be better
Whether or not they’re maintaining compliance and trends in data governance
How stable their products are and how well they continue to function
How well they’re minimizing, reducing and eliminating financial and operational risks
How well they collaborate across departments to provide actionable insights that yield better business outcomes

Factors that could impact development velocity include:

A lack of business alignment
Improper release and spring planning (13 Considerations For Agile Scrum Sprint Planning)
Code-to-production inefficiencies
Context switching and technical debt
Team dynamics and a failure to collaborate
Design issues and under-developed regression techniques

The engineering department is pivotal to most enterprise businesses. How well they do often impacts the greater business. From this standpoint, they’ll always strive for engineering excellence. Every metric drives impact, and data is the medium that will always move the needle.

Some of the best engineering teams in the world are constantly improving.
The goal of every successful engineering team is to get better together — to be more efficient, create impact and to deliver quality at every touch point of the customer journey. They want to know where they can make changes to become more agile, whether they need to upgrade their tools for performance and if it’s time to automate processes for sake of productivity.

How is data analytics used in engineering?

Data analysis involves gathering and studying data to form solid ideas and options that can be used when making game-changing decisions in any industry. All information derived from this process can be useful in several different ways, including building and developing business strategies, protecting the safety and security of the organization and ensuring the efficiency of an engineering project.

According to the University of California, “The data transformation part is fundamental to be a data-driven decision-making business by delivering data-as-a-product to data-consumers with embed data quality rules, including the removal of inaccurate, null or corrupted data entries, as well as the filtering and removal of irrelevant, duplicated, or confidential data.”

In this same sense, data governance becomes of equal importance.

Effective data governance ensures that all collected data is consistent and upheld with a sense of integrity, absent of misuse at any point of data analysis. It’s based on the organization’s internal data standards and policies and how engineering teams control its usage.

As it becomes increasingly important for businesses to adopt new data policy regulations, more and more firms are beginning to trust this data alone when making their most important decisions. And thus, new metrics are created for businesses that seek optimization.

Does my enterprise need an engineering analytics team?

Data is vital to any business or startup, whether they have an engineering analytics team or not. Data will always provide greater insight into what companies are doing right and what they can do better.

As a company grows, data too will expand, putting companies at risk of creating too many data sets or mismatching metrics that interfere with accuracy. Without direction, the amount of data can quickly escalate into something very complex and something very hard to maneuver.

These challenges only grow with the size and added complexity of available analytics.

With advanced analytics, there is no end to the possibilities of what a business can create or how it can improve. There’s only a limit to how well it is managed and the insights that are derived from it. And with new solutions on the horizon, some businesses are learning how to analyze data on their own.

But this still means physically building the right models for analyzing data and directing the right people to take the right action at the right time.

Automation, artificial intelligence and machine learning tools have made it increasingly possible for one person to do the work of many.

While larger enterprise businesses may have the need and resources available to hire a specialized team, smaller companies and startups need to act more efficiently. They lack the budget and manpower to fulfill one role in particular.

Enterprise-level data is very complex. There are a number of development methodologies to follow and a lot of processes to track. There are a number of dashboards to switch between and more DevOps tools than some teams can handle.

Between teams and technology, investments in data analysis can be very expensive — and they can all be maintained with an engineering-centric platform.

Where is engineering analytics headed?

The analytics engineer is an ever-evolving position by way of hybridization.

Analytics engineers, by default, provide business users with more reliable answers and more efficient data than any other data team member. But through new technologies, this role is also becoming redundant.

In fact, Jason Ganz, Manager of Developer Experience at dbt labs, believes that in five years, every organization will have some form of analytics engineering in place — but this doesn’t have to mean an actual engineer.

The art of engineering analytics is becoming more prevalent.

Data is becoming more efficient and easily accessible outside of traditional business silos. Through improved data literacy among non-technical people, business intelligence tools are increasingly being adopted for their real-time self-service capabilities.

By adopting engineering analytics or an engineering intelligence platform, businesses can freely build their own metrics and map out those metrics and arrive at a better software development lifecycle (SDLC) process. Plus, they’ll save both time and money — resources that can and should be placed elsewhere.

In the future, the “modern data team” will own the entire stack. In fact, it may even be the entire stack.

An engineering team should be able to set up its own tools and allow automation to write complex data transformations with layered business reports. There will be more reliable answers to very specific questions — especially those related to the business and future operations.

Automating analytics means faster, more reliable turnaround times and “all hands on deck” in the name of creativity. A scalable engineering intelligence platform should be SOC-2 Type II compliant and, at minimum, have the following:

Role-based access controls (RBAC)
Custom metrics and integrations
Exportable APIs
Flexible organizational hierarchy definition
Customizable roll-out templates and dashboards
Product and data security

What Are Engineering Analytics?

Data can significantly impact a company’s spending, improve customer touchpoints and steer teams into greater productivity.

What is analytics engineering?

Data engineer vs. analytics engineer – What’s the difference between the two?